There have been various proposals to provide autonomous or robotic machines for performing duties such as cleaning or polishing a floor area or for mowing grass. In their simplest form, an autonomous machine requires a training phase during which the machine is manually led around the area in which it is to work. Following this training phase, the autonomous machine will then perform the required work as it follows the path which it stored in its memory during the training phase. Other machines may simply follow a predetermined route which is marked by means such as a cable which is buried beneath the working area.
Other autonomous machines are supplied with a map of the environment in which they are to be used. The machine then uses this map to plan a route around the environment.
There have also been proposals for autonomous machines which are capable of exploring the environment in which they are placed without human supervision, and without advance knowledge of the layout of the environment. The machine may explore the environment during a learning phase and will subsequently use this information during a working phase. An autonomous machine shown in WO 00/38025 initially travels around the perimeter of an area, recognises when it has completed a single lap of the area, and then steps inwardly after that and subsequent laps of the room so as to cover the area in a spiral-like pattern. Autonomous machines are known to build a map of the working area using the information they acquire during the learning phase. Autonomous machines of this last type are particularly attractive to users as they can be left to work with minimal human supervision.
Autonomous machines usually have some form of odometry system for measuring the distance and direction travelled by the machine. Distance and direction information can be derived from sensors which monitor movement of each of the wheels. The machine uses the odometry information to deduce how far it has travelled since a starting position in the working area, and thus where it currently is located within the area. Unfortunately, relying on odometry information alone is unreliable as errors can quickly accumulate, and this can eventually lead to a complete disorientation of the machine. For example, if one of the drive wheels of the machine slips on the floor surface the odometry system will record a movement, since the wheel has turned, whereas, due to the wheel slippage, the machine does not actually move across the surface. Poor odometry information results in a difference between the calculated position of the machine and the actual position of the machine. In a floor cleaning machine this could result in the machine not travelling across some areas of the floor surface, which would remain dirty, or the machine becoming lost.
Odometry information can be supplemented, or replaced entirely, by other information. A paper entitled “Gyrodometry: A New Method for Combining Data from Gyros and Odometry in Mobile Robots” presented at the 1996 IEEE International Conference on Robotics and Automation, Minneapolis, Apr. 22–28, 1996, pp. 423–428, describes a proposal for reducing the problems of odometry-based robots in which the odometry data is substituted by gyro data during the short periods when odometry data is unreliable. Some systems position navigation beacons around an area such that the machine can calculate its position by a process of triangulating information received from a number of beacons. However, this has the obvious disadvantage of requiring beacons to be positioned around each area where the machine will work, and the associated cost of these beacons. U.S. Pat. No. 6,255,793 describes a system of this type where the boundary of the working area is defined by markers. One of the ways in which the calculated location of the autonomous machine can be corrected is by detecting the presence of markers which each have a unique identity.